This manuscript focuses on the modeling of electric vehicle (EV) driver's range anxiety, a
fear that a vehicle does not have sufficient range, or state of charge (SOC) of the battery …

Determination of the energy demand is of major importance in order to be able to unlock
optimization potential at system level. However, the traction energy demand of rail vehicles …

The development of accurate range prediction algorithms for battery electric vehicles (BEV)
is an important requirement for gaining the long-lasting confidence of future users in this …

The limited driving range is considered as a significant barrier to the spread of electric
vehicles. One effective method to reduce “range anxiety” is to offer accurate information to …

Electric vehicles (EVs) are an attractive alternative to conventional vehicles powered by
internal combustion engines due to their low carbon footprint, low running cost, and higher …

This study models the energy consumption of electric vehicles (EVs) under uncertain and
small data conditions by combining the machine learning method and the idea of controlled …

Accurate estimation of the power consumption of electric vehicles in the future driving route
will help to reduce the user's range anxiety, which results from short all electric range of …

Worldwide projections anticipate a fast-growing market share of the battery electric vehicles
(BEVs) to meet stringent emissions regulations for global warming and climate change. One …

A novel computational model for the volatile state was developed to improve eco-driving in
intelligent transportation systems (ITS). First, the volatile state was divided into eight types …

The development of a large scale agent-based simulation model for the Greater Boston Area
is presented, closing the gap between state-of-the art integrated demand-supply modeling …